{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5x^3-8x^2+3x-6=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.arange(-5,5,0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=5*np.power(x,3)-8*np.power(x,2)+3*x-6\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
